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FashionMNIST_TF1.ipynb Added speedup comparison project Oct 3, 2019
FashionMNIST_TF2.ipynb Omit unnecessary outputs Oct 3, 2019 Create Oct 3, 2019

In this mini-project, I present how eager execution can really be helpful in speeding up the model training process.

Steps I followed to conduct the experiments:

  • I maintained the exact same environment, model configuration, dataset (FashionMNIST) for the experiments. I only changed the TensorFlow versions.
  • I ran thorough profiling to check what really causes execution in TensorFlow 1.14 to be slow and I found out it was Sessions.

Apart from these, I used Weights and Biases to log the CPU usage and memory footprints of the experiments. I was amazed to find out that TensorFlow 2.0 was much more performant in terms of CPU usage as well. Here are some snaps:

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